Computations underlying sensorimotor learning.

The study of sensorimotor learning has a long history. With the advent of innovative techniques for studying learning at the behavioral and computational levels new insights have been gained in recent years into how the sensorimotor system acquires, retains, represents, retrieves and forgets sensorimotor tasks. In this review we highlight recent advances in the field of sensorimotor learning from a computational perspective. We focus on studies in which computational models are used to elucidate basic mechanisms underlying adaptation and skill acquisition in human behavior.This work was supported by the Wellcome Trust, the Human Frontiers Science Program, the Royal Society (Noreen Murray Professorship in Neurobiology to D.M.W.) and the Canadian Institutes of Health Research.This is the author accepted manuscript. The final version is available from Elsevier via http://dx.doi.org/10.1016/j.conb.2015.12.00

Q&A: Robotics as a tool to understand the brain.

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Seeing what you want to see: priors for one's own actions represent exaggerated expectations of success.

People perceive the consequences of their own actions differently to how they perceive other sensory events. A large body of psychology research has shown that people also consistently overrate their own performance relative to others, yet little is known about how these "illusions of superiority" are normally maintained. Here we examined the visual perception of the sensory consequences of self-generated and observed goal-directed actions. Across a series of visuomotor tasks, we found that the perception of the sensory consequences of one's own actions is more biased toward success relative to the perception of observed actions. Using Bayesian models, we show that this bias could be explained by priors that represent exaggerated predictions of success. The degree of exaggeration of priors was unaffected by learning, but was correlated with individual differences in trait optimism. In contrast, when observing these actions, priors represented more accurate predictions of the actual performance. The results suggest that the brain internally represents optimistic predictions for one's own actions. Such exaggerated predictions bind the sensory consequences of our own actions with our intended goal, explaining how it is that when acting we tend to see what we want to see.We thank J. D. Carlin for his help with acquiring eye gaze data. This work was funded by the Wellcome Trust [088324], Medical Research Council and a Scholar Award from the James S. McDonnell Foundation 21st Century Science Initiative: understanding human cognition (to James B. Rowe) as well as the Human Frontier Science Program and the Royal Society Noreen Murray Professorship in Neurobiology (to Daniel M. Wolpert); Noham Wolpe was funded by a Gates Cambridge Scholarship and the Raymond and Beverley Sackler Foundation.This is the final version of the article. It first appeared from Frontiers via http://dx.doi.org/10.3389/fnbeh.2014.0023

Attenuation of Self-Generated Tactile Sensations Is Predictive, not Postdictive

When one finger touches the other, the resulting tactile sensation is perceived as weaker than the same stimulus externally imposed. This attenuation of sensation could result from a predictive process that subtracts the expected sensory consequences of the action, or from a postdictive process that alters the perception of sensations that are judged after the event to be self-generated. In this study we observe attenuation even when the fingers unexpectedly fail to make contact, supporting a predictive process. This predictive attenuation of self-generated sensation may have evolved to enhance the perception of sensations with an external cause

The value of the follow-through derives from motor learning depending on future actions.

In ball sports, we are taught to follow through, despite the inability of events after contact or release to influence the outcome [1, 2]. Here we show that the specific motor memory active at any given moment critically depends on the movement that will be made in the near future. We demonstrate that associating a different follow-through movement with two motor skills that normally interfere [3-7] allows them to be learned simultaneously, suggesting that distinct future actions activate separate motor memories. This implies that when learning a skill, a variable follow-through would activate multiple motor memories across practice, whereas a consistent follow-through would activate a single motor memory, resulting in faster learning. We confirm this prediction and show that such follow-through effects influence adaptation over time periods associated with real-world skill learning. Overall, our results indicate that movements made in the immediate future influence the current active motor memory. This suggests that there is a critical time period both before [8] and after the current movement that determines motor memory activation and controls learning.This is the final published version. The article was originally published in Current Biology, Volume 25, Issue 3, p397–401, 2 February 2015, DOI: 10.1016/j.cub.2014.12.03

Mere Expectation to Move Causes Attenuation of Sensory Signals

When a part of the body moves, the sensation evoked by a probe stimulus to that body part is attenuated. Two mechanisms have been proposed to explain this robust and general effect. First, feedforward motor signals may modulate activity evoked by incoming sensory signals. Second, reafferent sensation from body movements may mask the stimulus. Here we delivered probe stimuli to the right index finger just before a cue which instructed subjects to make left or right index finger movements. When left and right cues were equiprobable, we found attenuation for stimuli to the right index finger just before this finger was cued (and subsequently moved). However, there was no attenuation in the right finger just before the left finger was cued. This result suggests that the movement made in response to the cue caused ‘postdictive’ attenuation of a sensation occurring prior to the cue. In a second experiment, the right cue was more frequent than the left. We now found attenuation in the right index finger even when the left finger was cued and moved. This attenuation linked to a movement that was likely but did not in fact occur, suggests a new expectation-based mechanism, distinct from both feedforward motor signals and postdiction. Our results suggest a new mechanism in motor-sensory interactions in which the motor system tunes the sensory inputs based on expectations about future possible actions that may not, in fact, be implemented

Human decision making anticipates future performance in motor learning.

It is well-established that people can factor into account the distribution of their errors in motor performance so as to optimize reward. Here we asked whether, in the context of motor learning where errors decrease across trials, people take into account their future, improved performance so as to make optimal decisions to maximize reward. One group of participants performed a virtual throwing task in which, periodically, they were given the opportunity to select from a set of smaller targets of increasing value. A second group of participants performed a reaching task under a visuomotor rotation in which, after performing a initial set of trials, they selected a reward structure (ratio of points for target hits and misses) for different exploitation horizons (i.e., numbers of trials they might be asked to perform). Because movement errors decreased exponentially across trials in both learning tasks, optimal target selection (task 1) and optimal reward structure selection (task 2) required taking into account future performance. The results from both tasks indicate that people anticipate their future motor performance so as to make decisions that will improve their expected future reward

Multiple motor memories are learned to control different points on a tool.

Skillful object manipulation requires learning the dynamics of objects, linking applied force to motion 1 ,2 . This involves the formation of a motor memory 3 ,4 , which has been assumed to be associated with the object, independent of the point on the object that one chooses to control. Importantly, in manipulation tasks, different control points on an object, such as the rim of a cup when drinking or its base when setting it down, can be associated with distinct dynamics. Here we show that opposing dynamic perturbations, which interfere when controlling a single location on an object, can be learned when each is associated with a separate control point. This demonstrates that motor memory formation is linked to control points on the object, rather than the object per se . We also show that the motor system only generates separate memories for different control points if they are linked to different dynamics, allowing efficient use of motor memory. To account for these results, we develop a normative switching state-space model of motor learning, in which the association between cues (control points) and contexts (dynamics) is learned rather than fixed. Our findings uncover an important mechanism through which the motor system generates flexible and dexterous behavior

Motor memories of object dynamics are categorically organized.

The ability to predict the dynamics of objects, linking applied force to motion, underlies our capacity to perform many of the tasks we carry out on a daily basis. Thus, a fundamental question is how the dynamics of the myriad objects we interact with are organized in memory. Using a custom-built three-dimensional robotic interface that allowed us to simulate objects of varying appearance and weight, we examined how participants learned the weights of sets of objects that they repeatedly lifted. We find strong support for the novel hypothesis that motor memories of object dynamics are organized categorically, in terms of families, based on covariation in their visual and mechanical properties. A striking prediction of this hypothesis, supported by our findings and not predicted by standard associative map models, is that outlier objects with weights that deviate from the family-predicted weight will never be learned despite causing repeated lifting errors